Szczegóły publikacji
Opis bibliograficzny
Towards sustainable buildings and energy communities: AI-driven transactive energy, smart local microgrids, and life cycle integration / Andrzej OŻADOWICZ // Energies [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1073 . — 2025 — vol. 18 iss. 21 art. no. 5668, s. 1–31. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 26–31, Abstr. — Publikacja dostępna online od: 2025-10-29
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164191 |
|---|---|
| Data dodania do BaDAP | 2025-11-18 |
| Tekst źródłowy | URL |
| DOI | 10.3390/en18215668 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Energies |
Abstract
The transition towards sustainable and low-carbon energy systems highlights the crucial role of buildings, microgrids, and local communities as key actors in enhancing resilience and achieving decarbonization targets. The application of artificial intelligence (AI) is of paramount importance as it enables accurate prediction, adaptive control, and optimization of distributed resources. This paper reviews recent advances in AI applications for transactive energy (TE) and dynamic energy management (DEM), focusing on their integration with building automation, microgrid coordination, and community energy exchanges. It also considers the emerging role of life cycle-based methods, such as life cycle assessment (LCA) and life cycle cost (LCC), in extending operational intelligence to long-term environmental and economic objectives. The analysis is based on a curated set of 97 publications identified through structured queries and thematic filtering. The findings indicate substantial advancement in methodological approaches, notably reinforcement learning (RL), hybrid model predictive control, federated and edge AI, and digital twin applications. However, this study also uncovers shortcomings in the integration and interoperability of sustainability. This paper contributes by consolidating fragmented research and proposing a multi-layered AI framework that aligns short-term performance with long-term resilience and sustainability.
